Learning from demonstrations is a powerful paradigm for robot manipulation, but its effectiveness hinges on both the quantity and quality of the collected data. In this work, we present a case study of how instrumentation, i.e. integration of sensors, can improve the quality of demonstrations and automate data collection. We instrument a squeeze bottle with a pressure sensor to learn a liquid dispensing task, enabling automated data collection via a PI controller. Transformer-based policies trained on automated demonstrations outperform those trained on human data in 78% of cases. Our findings indicate that instrumentation not only facilitates scalable data collection but also leads to better-performing policies, highlighting its potential in the pursuit of generalist robotic agents.
View on arXiv@article{proesmans2025_2504.18481,
title={ Instrumentation for Better Demonstrations: A Case Study },
author={ Remko Proesmans and Thomas Lips and Francis wyffels },
journal={arXiv preprint arXiv:2504.18481},
year={ 2025 }
}